This invention discloses a
computer vision-based method and
system for detecting the workability of concrete. It employs a non-contact visual sensing approach to simultaneously acquire video sequences of freshly mixed concrete in a free-flowing state at both the macroscopic
slump flow scale and the microscopic particle migration scale. An improved spatiotemporal feature
pyramid network is used to extract multi-scale morphological features from the video sequences, obtaining a multi-scale visual
feature set including the flow profile evolution rate, surface
ripple spectrum, aggregate suspension trajectory, and paste
coating state. A graph neural
network model integrating prior knowledge of fluid
mechanics is constructed to map discrete visual features to continuous rheological parameters, outputting workability indicators such as
slump, spread, yield stress, and
plastic viscosity in real time. This invention significantly improves the level of intelligent
quality control in concrete production.